
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
      from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase     =get_tests_dir("""fixtures/test_sentencepiece.model""")

_lowerCamelCase     ={"target_lang": "fi", "source_lang": "en"}
_lowerCamelCase     =">>zh<<"
_lowerCamelCase     ="Helsinki-NLP/"

if is_torch_available():
      _lowerCamelCase     ="pt"
elif is_tf_available():
      _lowerCamelCase     ="tf"
else:
      _lowerCamelCase     ="jax"





@require_sentencepiece
class 				A__						(      _SCREAMING_SNAKE_CASE    ,  unittest.TestCase):
      _UpperCAmelCase							:       Tuple       			=     MarianTokenizer
      _UpperCAmelCase							:       str       			=     False
      _UpperCAmelCase							:       Union[str, Any]       			=     True
      def 		UpperCamelCase__				( self ):
       super().setUp()
       lowerCamelCase						:      Any							     = ['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
       lowerCamelCase						:      Dict							     = dict(zip(__UpperCAmelCase       ,    range(len(__UpperCAmelCase ) ) ) )
       lowerCamelCase						:      List[str]							     = Path(self.tmpdirname )
       save_json(__UpperCAmelCase       ,    save_dir / VOCAB_FILES_NAMES["""vocab"""] )
       save_json(__UpperCAmelCase       ,    save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""] )
       if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
        copyfile(__UpperCAmelCase       ,    save_dir / VOCAB_FILES_NAMES["""source_spm"""] )
        copyfile(__UpperCAmelCase       ,    save_dir / VOCAB_FILES_NAMES["""target_spm"""] )

       lowerCamelCase						:      Optional[int]							     = MarianTokenizer.from_pretrained(self.tmpdirname )
       tokenizer.save_pretrained(self.tmpdirname )
      def 		UpperCamelCase__				( self       ,    **__magic_name__ ):
       return MarianTokenizer.from_pretrained(self.tmpdirname       ,    **__UpperCAmelCase )
      def 		UpperCamelCase__				( self       ,    __magic_name__ ):
       return (
           "This is a test",
           "This is a test",
       )
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      List[Any]							     = '''</s>'''
       lowerCamelCase						:      str							     = 0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase )       ,    __UpperCAmelCase )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase )       ,    __UpperCAmelCase )
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      Dict							     = list(self.get_tokenizer().get_vocab().keys() )

       self.assertEqual(vocab_keys[0]       ,    """</s>""" )
       self.assertEqual(vocab_keys[1]       ,    """<unk>""" )
       self.assertEqual(vocab_keys[-1]       ,    """<pad>""" )
       self.assertEqual(len(__UpperCAmelCase )       ,    9 )
      def 		UpperCamelCase__				( self ):
       self.assertEqual(self.get_tokenizer().vocab_size       ,    9 )
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      List[Any]							     = MarianTokenizer.from_pretrained(F'''{ORG_NAME}opus-mt-en-de''' )
       lowerCamelCase						:      Union[str, Any]							     = en_de_tokenizer(["""I am a small frog"""]       ,    return_tensors=__UpperCAmelCase )
       self.assertIsInstance(__UpperCAmelCase       ,    __UpperCAmelCase )
       lowerCamelCase						:      Optional[int]							     = [3_8, 1_2_1, 1_4, 6_9_7, 3_8_8_4_8, 0]
       self.assertListEqual(__UpperCAmelCase       ,    batch.input_ids[0] )

       lowerCamelCase						:      List[Any]							     = tempfile.mkdtemp()
       en_de_tokenizer.save_pretrained(__UpperCAmelCase )
       lowerCamelCase						:      str							     = [x.name for x in Path(__UpperCAmelCase ).glob("""*""" )]
       self.assertIn("""source.spm"""       ,    __UpperCAmelCase )
       MarianTokenizer.from_pretrained(__UpperCAmelCase )
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      List[str]							     = self.get_tokenizer()

       lowerCamelCase						:      Any							     = tok(
           ["""I am a small frog""" * 1_0_0_0, """I am a small frog"""]       ,    padding=__UpperCAmelCase       ,    truncation=__UpperCAmelCase       ,    return_tensors=__UpperCAmelCase )
       self.assertIsInstance(__UpperCAmelCase       ,    __UpperCAmelCase )
       self.assertEqual(batch.input_ids.shape       ,    (2, 5_1_2) )
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      Optional[int]							     = self.get_tokenizer()
       lowerCamelCase						:      Union[str, Any]							     = tok(["""I am a tiny frog""", """I am a small frog"""]       ,    padding=__UpperCAmelCase       ,    return_tensors=__UpperCAmelCase )
       self.assertIsInstance(__UpperCAmelCase       ,    __UpperCAmelCase )
       self.assertEqual(batch_smaller.input_ids.shape       ,    (2, 1_0) )
      @slow
      def 		UpperCamelCase__				( self ):
       # fmt: off
       lowerCamelCase						:      Tuple							     = {'''input_ids''': [[4_3_4_9_5, 4_6_2, 2_0, 4_2_1_6_4, 1_3_6_9, 5_2, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 7_4_9_1, 3_8_9_9_9, 6, 8, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 4_6_6_9, 3_7_8_6_7, 1_3, 7_5_2_5, 2_7, 1_5_9_3, 9_8_8, 1_3, 3_3_9_7_2, 7_0_2_9, 6, 2_0, 8_2_5_1, 3_8_3, 2, 2_7_0, 5_8_6_6, 3_7_8_8, 2, 2_3_5_3, 8_2_5_1, 1_2_3_3_8, 2, 1_3_9_5_8, 3_8_7, 2, 3_6_2_9, 6_9_5_3, 1_8_8, 2_9_0_0, 2, 1_3_9_5_8, 8_0_1_1, 1_1_5_0_1, 2_3, 8_4_6_0, 4_0_7_3, 3_4_0_0_9, 2_0, 4_3_5, 1_1_4_3_9, 2_7, 8, 8_4_6_0, 4_0_7_3, 6_0_0_4, 2_0, 9_9_8_8, 3_7_5, 2_7, 3_3, 2_6_6, 1_9_4_5, 1_0_7_6, 1_3_5_0, 3_7_8_6_7, 3_2_8_8, 5, 5_7_7, 1_0_7_6, 4_3_7_4, 8, 5_0_8_2, 5, 2_6_4_5_3, 2_5_7, 5_5_6, 4_0_3, 2, 2_4_2, 1_3_2, 3_8_3, 3_1_6, 4_9_2, 8, 1_0_7_6_7, 6, 3_1_6, 3_0_4, 4_2_3_9, 3, 0], [1_4_8, 1_5_7_2_2, 1_9, 1_8_3_9, 1_2, 1_3_5_0, 1_3, 2_2_3_2_7, 5_0_8_2, 5_4_1_8, 4_7_5_6_7, 3_5_9_3_8, 5_9, 3_1_8, 1_9_5_5_2, 1_0_8, 2_1_8_3, 5_4, 1_4_9_7_6, 4_8_3_5, 3_2, 5_4_7, 1_1_1_4, 8, 3_1_5, 2_4_1_7, 5, 9_2, 1_9_0_8_8, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0], [3_6, 6_3_9_5, 1_2_5_7_0, 3_9_1_4_7, 1_1_5_9_7, 6, 2_6_6, 4, 4_5_4_0_5, 7_2_9_6, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=__UpperCAmelCase       ,    model_name="""Helsinki-NLP/opus-mt-en-de"""       ,    revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""       ,    decode_kwargs={"""use_source_tokenizer""": True}       ,    )






      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      str							     = MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs""" )

       lowerCamelCase						:      str							     = '''Tämä on testi'''
       lowerCamelCase						:      int							     = '''This is a test'''

       lowerCamelCase						:      str							     = [7_6, 7, 2_0_4_7, 2]
       lowerCamelCase						:      List[str]							     = [6_9, 1_2, 1_1, 9_4_0, 2]

       lowerCamelCase						:      Optional[Any]							     = tokenizer(__UpperCAmelCase ).input_ids
       self.assertListEqual(__UpperCAmelCase       ,    __UpperCAmelCase )

       lowerCamelCase						:      List[Any]							     = tokenizer(text_target=__UpperCAmelCase ).input_ids
       self.assertListEqual(__UpperCAmelCase       ,    __UpperCAmelCase )

       lowerCamelCase						:      Dict							     = tokenizer.decode(__UpperCAmelCase       ,    skip_special_tokens=__UpperCAmelCase )
       self.assertEqual(__UpperCAmelCase       ,    __UpperCAmelCase )







import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       Any:
														super().__init__()
														self.register_modules(vqvae=__UpperCAmelCase    ,						unet=__UpperCAmelCase    ,						scheduler=__UpperCAmelCase						)






							@torch.no_grad()
							def __call__(					self    ,						__UpperCAmelCase = 1    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = 0.0    ,						__UpperCAmelCase = 50    ,						__UpperCAmelCase = "pil"    ,						__UpperCAmelCase = True    ,						**__UpperCAmelCase    ,						)		->       Union[Tuple, ImagePipelineOutput]:
														A      :					List[Any]      		=					randn_tensor(
														    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,						generator=__UpperCAmelCase    ,						)
														A      :					List[Any]      		=					latents.to(self.device						)

														# scale the initial noise by the standard deviation required by the scheduler
														A      :					Optional[int]      		=					latents * self.scheduler.init_noise_sigma

														self.scheduler.set_timesteps(__UpperCAmelCase						)

														# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
														A      :					str      		=					'''eta''' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)

														A      :					List[str]      		=					{}
														if accepts_eta:
																					A      :					Optional[Any]      		=					eta

														for t in self.progress_bar(self.scheduler.timesteps						):
																					A      :					Any      		=					self.scheduler.scale_model_input(__UpperCAmelCase    ,						__UpperCAmelCase						)
																					# predict the noise residual
																					A      :					Dict      		=					self.unet(__UpperCAmelCase    ,						__UpperCAmelCase						).sample
																					# compute the previous noisy sample x_t -> x_t-1
																					A      :					int      		=					self.scheduler.step(__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						**__UpperCAmelCase						).prev_sample

														# decode the image latents with the VAE
														A      :					str      		=					self.vqvae.decode(__UpperCAmelCase						).sample

														A      :					Tuple      		=					(image / 2 + 0.5).clamp(0    ,						1						)
														A      :					int      		=					image.cpu().permute(0    ,						2    ,						3    ,						1						).numpy()
														if output_type == "pil":
																					A      :					Union[str, Any]      		=					self.numpy_to_pil(__UpperCAmelCase						)

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=__UpperCAmelCase						)






